Research Scientist, Gemini Commercial Intelligence, Deepmind

Google Google · Big Tech · Mountain View, CA +1

Research Scientist at Google DeepMind working on Gemini models for commercial intelligence, focusing on natural language and multimodal understanding/generation, planning, reasoning, reinforcement learning, and agents. The role involves driving research from conception to production, including experimentation, prototyping, and evaluation, with a focus on LLMs and related AI fields.

What you'd actually do

  1. Work with experts on LLMs, multi-modal, recommendation systems, personalization, and ML efficiency.
  2. Drive new research ideas from conception, experimentation, to production in a rapidly shifting landscape.
  3. Drive project work by defining the data structure, framework, design, and evaluation metrics for research solution development and implementation. Identify timelines and obtain resources needed.
  4. Identify new and upcoming research areas by interacting with potential external and internal collaborators. Help in developing long-term research strategy and plans to expand the impact of Google research.

Skills

Required

  • Bachelor’s degree or equivalent practical experience
  • 2 years of experience building and shipping technical products
  • 2 years of experience leading a research agenda
  • Experience in the domain area of generative AI and Large Language Models (LLM)

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • 3 years of research experience (e.g., model training/deployment, efficiency optimization, data pipeline design, ML infra)
  • 2 years of experience leading research efforts and influencing other researchers
  • One or more scientific publication submission(s) for conferences, journals, or public repositories

What the JD emphasized

  • building and shipping technical products
  • leading a research agenda
  • generative AI and Large Language Models (LLM)
  • research experience (e.g., model training/deployment, efficiency optimization, data pipeline design, ML infra)
  • leading research efforts and influencing other researchers
  • scientific publication submission(s)

Other signals

  • Gemini models
  • LLMs
  • multimodal
  • recommendation systems
  • personalization
  • ML efficiency
  • research to production
  • planning and reasoning
  • reinforcement learning
  • agents